CONSUMER ACCEPTANCE OF MOBILE PAYMENTS: AN EXPLORATORY STUDY A THESIS to be submitted by BHAVIT KUMAR TRIPATHI for the award of the degree of MASTER OF SCIENCE (by Research) DEPARTMENT OF MANAGEMENT STUDIES INDIAN INSTITUTE OF TECHNOLOGY MADRAS JUNE 2014

1. SYNOPSIS OF
CONSUMER ACCEPTANCE OF MOBILE PAYMENTS:
AN EXPLORATORY STUDY
A THESIS
to be submitted by
BHAVIT KUMAR TRIPATHI
for the award of the degree
of
MASTER OF SCIENCE
(by Research)
DEPARTMENT OF MANAGEMENT STUDIES
INDIAN INSTITUTE OF TECHNOLOGY MADRAS
JUNE 2014

2. 2
1. INTRODUCTION
Mobile payments or M-Payments refer to the payment services performed from or via a mobile
device. The four primary models for Mobile payments are premium SMS, direct mobile billing,
web based payments (including apps) and contactless Near Field Communication (NFC). The
first instance of Mobile payments occurred in 1997 when Coca Cola introduced a limited number
of vending machines where a customer could make a mobile purchase. The customer would send
a text to the vending machine to setup payment and the machine would then vend their product.
Mobile banking first appeared in 1997, introduced by the Merita Bank. It accepted text messages
for making bank account transactions.
Mobile commerce (M- Commerce) involves the sale of goods, services and contents via wireless
devices, without time or space limitations (Au and Kauffman, 2008; Mallat, 2007). Mobile
payment is an emerging and important part of mobile commerce (Yang, 2012). Mobile payment
is one of the most critical drivers for successful mobile commerce (Yang, 2012). As mobile
commerce increases in adoption, Mobile payment will continue to facilitate secure electronic
commercial transactions between organizations and individuals (Ondrus and Pigneur, 2006).
Mobile Payments fall broadly into two categories; payments for purchases and payments of
bills/invoices (Karnouskos and Fokus, 2004). In payments for purchases, Mobile payments
compete with or complement cash, checks, credit cards and debit cards. In payments of
bills/invoices, Mobile payments typically provide access to account-based payments, including
money transfers, online banking payments or direct debit assignments.
2. MOTIVATION
In developing economies, the growth in M-Payments is being largely driven by the huge
population of unbanked consumers, who can get access to payment services options through
mobile devices. The development of new mobile technologies increases day after day and creates
important opportunities for commerce. Mobile commerce is now taking place in the market,
introducing mobile payment as a new transaction method. Given the widespread use of mobile
devices and users’ needs for convenient timely payment, mobile payment is expected to become
an important channel for conducting financial transactions. As an emerging service, mobile

3. 3
payment has not received wide acceptance among users. Thus, mobile payment user behavior
has received some research attention (Dahlberg et al., 2006).
3. RELATED LITERATURE
Mobile payment refers to a payment for goods, services, and bills using a mobile device using
wireless and other communication technologies (Dahlberg, Mallat, Ondrus, and Zmijewska,
2008). Researchers have been concerned with mobile payment user behavior and have tried to
identify the factors affecting user acceptance of mobile payment technology. Since the beginning
of this decade M-Payment has received extensive attention from both academics and
practitioners (Dahlberg et al. 2006). Scholars have conceptualized success factors (Zmijewska
and Lawrence 2005), analyzed empirically users’ acceptance (Dahlberg et al. 2003), examined
different enabling technologies (Zmijewska, 2005), evaluated the disruptive potential of M-
Payments against other payment instruments (Ondrus and Pigneur, 2005) and analyzed the
emerging industry from a value-based perspective (Pousttchi, 2008). Recent research literature
on mobile payment acceptance includes studies on market and stakeholder analysis (Ondrus and
Pigneur, 2006), consumer acceptance models (Pouttchi, 2003), country assessment (Dewan and
Chen ,2005) and comparison across multiple countries (Au and Zafar, 2008). In particular, the
mobile user’s intention to use mobile payment is of considerable interest to researchers and
practitioners, because financial institutions, trusted third parties, payment service providers, and
supporting service providers can benefit greatly from enhanced understanding of the key factors
underlying mobile users’ intention (Dahlberg, Mallat, and Öörni, 2003a; Dahlberg, Mallat, and
Öörni, 2003b; Lim, 2007; Ondrus and Pigneur, 2006).
A number of studies have focused on the acceptance factors of M-Payment. These studies have
used TAM, IDT and UTAUT with suitable extensions. Most of the studies are based primarily
on the TAM, with additional constructs adapted for the study of M-Payment such as security,
cost, trust, mobility, expressiveness, convenience, speed of transaction, use situation, social
reference groups, facilitating condition, the attractiveness of alternatives, privacy, system quality
and technology anxiety (Chen and Adams, 2005; Cheong, Park and Hwang, 2004; Dahlberg,
Mallat, Penttinen, and Sohlberg, 2002; Dahlberg et al., 2003a; Dahlberg et al., 2003b; Dewan
and Chen, 2005; Mallat, 2004; Mallat and Dahlberg, 2005; Torsten, Gerpott, and Kornmeier,

4. 4
2009; Valcourt, Robert and Beaulieu, 2005; Zmijewska, Lawrence, and Steele, 2004b). There
are limited studies with combination of IDT and TAM (Chen, 2008) and UTAUT with the
additional constructs of perceived risk, user’s cost and use context (Peng, 2011; Wang and Yi,
2012). In their qualitative study Mallat (2007) noted that relative advantage, compatibility,
complexity, costs, trust and perceived risk affect user adoption of mobile payment.
Reviewing the relevant literature, we find that only a limited understanding exists about the
drivers of mobile payment acceptance. Empirical research in this area has been quite scarce.
Recently a more integrated and consumer oriented model for the study of user acceptance of
technology has been proposed (UTAUT2: Venkatesh, 2012). Further, previous research has
reported a positive association between level of education and user’s attitude towards technology
(Zmud, 1979; Igbaria, 1989; Lucas, 1978) by providing a stock of knowledge that enables more
effective and adaptive learning (Ashcraft, 2002). However previous studies in mobile payment
do not use this variable to the best of our knowledge.
4. RESEARCH GAPS
Four key research gaps emerge after the review of literature: (a) user acceptance of mobile
payments has been scarcely addressed empirically in literature (b) mobile payment adoption has
been addressed very scarcely in the Indian context to the best of our knowledge (c) although
some studies have used technology acceptance models in mobile payments, more recent and
unified models like the UTAUT2 has not been used (d) education level which could be a very
influential factor has not been included in previous models studying mobile payment acceptance.
This research aims to address the above gaps by conducting an exploratory study to guide further
empirical investigation.
5. RESEARCH OBJECTIVES
This research seeks to address the gap in academic research in addressing user acceptance of
mobile payments in India. Two key research objectives which are addressed by this research are:
i. Study consumer acceptance of Mobile Payment Systems using UTAUT2 model
ii. Develop and test suitable moderator(s) in the specific context of Mobile Payments

5. 5
6. RESEARCH MODEL AND HYPOTHESES DEVELOPMENT
Drawing on Venkatesh et al. (2012), we study the direct effects of facilitating conditions,
hedonic motivation, price value and habit on the behavioural intention to use and the moderating
effects of age, gender, experience and education level on these relationships. Following Igbaria
(1989) and Lucas (1978), we use education level as an additional moderating variable, given the
context of smart phone usage for mobile payments. Fig. 1 provides a schematic representation of
our proposed model.
Fig. 1 Proposed Research Model
We develop our hypotheses based on the four antecedent variables facilitating conditions,
hedonic motivation, price value and habit and four moderating variables age, gender, experience
and education level. We propose education level as another moderating variable in the extended
UTAUT2 model.
H1a: Facilitating conditions is positively related to behavioral intention to use mobile payments.

6. 6
H1b: Age, gender, experience and education level will moderate the effect of facilitating
conditions on behavioral intention, such that the effect will be stronger among older women with
less experience and having higher education level.
H2a: Hedonic motivation is positively related to behavioral intention to use mobile payment.
H2b: Age, gender, and experience will moderate the effect of hedonic motivation on behavioral
intention such that the effect will be stronger among younger men in early stages of experience
with a technology.
H3a: Price value is positively related to behavioral intention to use mobile payment.
H3b: Age and gender will moderate the effect of price value on behavioral intention, such that
the effect will be stronger among older women.
H4a: Habit is positively related to behavioral intention to use mobile payment.
H4b: Age, gender and experience will moderate the effect of habit on behavioral intention such
that the effect will stronger for older men with high levels of experience with the technology
7. RESEARCH METHODOLOGY
7.1 Development of the Survey Instrument
We developed a survey questionnaire suitable for the measurement of variables included in the
model. The scales used to measure the variables used in the study have been adopted from
previous research with established reliability and validity. Some of the items were modified to
contextualize them relevant to mobile payment research. The constructs were measured using
multiple item scales. In addition to the four moderating variables, the research model includes
seven independent variables and one outcome variable. Each construct was measured with
multiple items on a seven point likert scale with 1- strongly disagree to 7-strongly agree. The
multiple phases of instrument development resulted in the refinement and restructuring of the
survey instrument, as well as the establishment of the initial face validity and internal validity of
the measures (Nunnally, 1978).
7.2 Target Audience and Sampling Procedure
The study used purposive sampling technique which is also known as judgmental sampling. The
target population in this research consisted of young adults who use mobile phones for electronic

7. 7
payments. The sample frame consisted of undergraduate and post graduate students of a premier
Educational Institute representing this population. University students were targeted for this
study because they form a major user group of the mobile phones and mobile networks (CNNIC,
2010) and they may be more willing to accept mobile payments (Scevak, 2010). The target
audience owns the mobile phones and also uses them for mobile payments. To ensure that the
measured beliefs were based on direct behavioral experience with the object, only responses
from those who had previously used the mobile payment were included in our analysis.
8. DATA ANALYSES
8.1 Data Preparation and Descriptive Statistics
We entered the data obtained from survey into a personal computer and checked the data for
missing data. Approximately 27% of the responses were dropped due to missing data. Finally,
257 responses were used for empirical analysis. With regard to gender, the sample consists of
58% male and 41.2% female. In terms of age, the majority of the respondents, 66%, are between
the age group of 19-25 years. With regard to education level 58.4% are undergraduates, 30.7%
are graduates and10.9% are postgraduates. A significant number of the respondents use mobile
payment 1-4 times per month and have 1-2 years of experience in mobile payment use.
Partial least square structural equation modelling (PLS- SEM) was used in this study to test the
hypotheses. The reason for choose PLS- SEM because, this research is exploratory in nature and
has a relatively small sample size. R- software package version 3.01 with plspm add on package
for PLS path modelling was used for data analysis. Bootstrapping technique with 1,000
resamples was employed to determine the significance levels for loadings, weights and path
coefficients (Hair et al., 2011). Measurement model estimation involves confirmatory factor
analysis (CFA) and reports results of construct uni-dimensionality, validity and reliability.
Structural model identifies the relationship present among the latent variables. Latent variables
are the constructs or determinants present in the model and manifest variables are items that load
on the latent variable as measures. Generally, the manifest variables loadings are measured in

8. 8
measurement model and the structural relationship among the latent variables is estimated in
structural model.
8.2 Measurement Model
To evaluate the measurement model, we followed the procedures outlined by Hair et al. (2011).
Three types of validity tests were carried out to validate the reflective constructs: internal
consistency, convergent validity and discriminant validity. Measurement model estimation
involved confirmatory factor analysis (CFA) and reported results of construct uni-
dimensionality, validity and reliability. The results are presented in Table 1. The value of
Cronbach’s alpha ranged from 0.713 to 0.884, which were above the acceptable value of 0.6
(Hair et al., 2011). Statistical evidence for unidimensionality and convergent validity were also
checked through Dillon-Goldstein’s , Average Variance Extracted (AVE), first and second
Eigen values and factor loadings (Table 1). All Dillon-Goldstein’s were found to be above 0.6
and the difference between first and second eigen values exceeded 1 (Tenenhaus et al., 2005),
showing evidence for convergent validity.
Table 1 Reliability and Validity Testing (N=257)
MVs C.alpha DG.rho Eigen
1st
Eigen
2nd
Standard loadings AVE
Performance
Expectancy (PER)
4 0.832 0.889 2.67 0.636 0.867,0.738,0.866,
0.785
0.665
Effort expectancy
(EFF)
4 0.884 0.920 2.97 0.416 0.878,0.895,0.839,
0.832
0.742
Social Influence
(SOC)
3 0.684 0.826 1.84 0.654 0.891,0.679,0.732 0.592
Facilitating
conditions
(FAC)
4 0.847 0.900 2.78 0.642 0.750,0.780,0.797,
0.984
0.694
Hedonic
Motivation
(HED)
3 0.686 0.828 1.85 0.699 0.838,0.762,0.725 0.604
Price value (PRV) 3 0.771 0.870 2.08 0.772 0.698,0.836,0.942 0.692
Habit (HAB) 4 0.747 0.842 2.29 0.742 0.774,0.848
,0.724 0.665
0.57
Behavior Intention
(BHV)
3 0.713 0.839 1.91 0.561 0.807,0.776,0.807 0.689

9. 9
Convergent validity was further tested using the Average Variance Extracted (AVE) as shown in
Table 2. The suggested cut off value of AVE is 0.50 or higher (Fornell and Lacker, 1981; Hair et
al., 2011). For sufficient discriminant validity, an indicator’s loadings and the square root of the
AVE should exceed the values of both horizontal and vertical correlation between variables
(Chin, 1998; Hair et al., 2011).
Table 2 Discriminant Validity (N=257)
PER EFF SOC FAC HED PRV HAB BHV
PER 0.816
EFF 0.220 0.861
SOC 0.726 0.241 0.772
FAC 0.445 0.233 0.476 0.832
HED 0.507 0.223 0.420 0.393 0.775
PRV 0.445 0.280 0.498 0.429 0.520 0.830
HAB 0.584 0.361 0.612 0.472 0.514 0.601 0.755
BHV 0.603 0.380 0.503 0.484 0.509 0.490 0.583 0.796
8.2.1 Common Method Bias
Factor analysis results suggest the presence of seven factors accounting for a total of 69.45% of
the variance, of which the first factor accounted for 34.08% of the variance. Since a single factor
does not emerge and does not account for the majority of variance in the variables, it is
reasonable to conclude that common method bias is not a significant problem in this study
(Malhotra et al., 2006; Podsakoff et al., 2003).
9. RESULTS
Table 3 presents the estimates obtained from the PLS path modeling. The R2
value of 0.504
indicates that the model explains a substantial amount variance in the of behavioral intention to
use mobile payment. In support of H1a, we found a significant and positive relationship between
facilitating conditions and the intention to use mobile payments (H1a: β=0.148; p ≤ 0.05).
Similarly, Price value and Habit also had positive and significant effect on behavioural intention
(H3a: β=0.06; p ≤ 0.05; H4a: β=0.21; p ≤ 0.05). However, unlike the results reported by
UTAUT2, our analysis did not show a significant positive relationship between Hedonic
motivation and behavioural intention (H2a). We also studied moderation effects by subgroup
comparison method. It was proposed that the facilitating conditions to behavior intention would
be moderated by age, gender, experience and education level. Our results show that Facilitating
conditions to Behavioral Intention relationship is moderated by education and hence that

10. 10
hypothesis in H1b is supported (diff β=0.291; p ≤ 0.05). Similarly we found support for the
hypothesized moderating relationship H2b of age on the Hedonic motivation to behavioural
intention relationship (diff β= 0.1.038; p ≤ 0.05). However all other moderating relationships of
age, gender, experience and education level with other direct relationships were was not
supported.
Table 3 Structural Model (N=257)
Relationship (Dependant variable:
Behavioral Intention-BHV)
Originalβ
(n=257)
Boot β
(n=1000)
Grp1 β Grp2 β
Grp
abs.dif
Remarks
Performance Expectancy (PER) 0.28 0.291* - - -
Effort Expectancy (EFF) 0.174 0.170* - - -
Social influence (SOC) -0.064 -0.618 - - -
Facilitating conditions (FAC) 0.145 0.148* - - -
H1a
supported
Hedonic motivation (HED) 0.168 0.167 - - -
H2a not
supported
Price value (PRV) 0.056 0.061* - - -
H3a
supported
Habit (HAB) 0.208 0.208* - - -
H4a
supported
MODERATING EFFECTS (by group comparisons)
Facilitating Conditions to BHV relationship (FACBHV) [Hypothesis H1b]
Age (AGE)
Grp1- low (<= 25,n1 = 169)
Grp 2- high (> 25 ,n2 = 88)
- - -0.005 0.180 0.185
Not
supported
Gender (GND)
Grp1- Male (n1=151),
Grp2-Female (n2=106)
- - 0.847 0.115 0.732
Not
supported
Experience (EXP)
Grp1-low (<= 1 year, n1= 88)
Grp 2- high (> 1 year, n2= 169)
- - 0.261 0.138 0.123
Not
supported
Education Level (EDL)
Grp 1- low ( Undergrad,n1==150)
Grp 2- high (Postgrad & above
n2=107)
- - -0.029 0.261
0.291*
Supported
Hedonic Motivation to BHV relationship (HEDBHV) [Hypothesis H2b]
Age (AGE)
Grp1- low (<= 25yrs,n1 = 169)
Grp 2- high (> 25yrs ,n2 = 88)
- - 0.191 0.169 0.023
Not
supported
Gender (GND)
Grp1- Male (n1=151)
Grp2-Female (n2=106)
- - -0.247 0.791 1.038* Supported
Experience (EXP)
Grp1-low (<= 1 year, n1= 88)
- - 0.252 0.162 0.090
Not
supported

11. 11
Grp 2- high (> 1 year, n2= 169)
Education Level (EDL)
Grp 1- low ( Undergrad,n1==150)
Grp 2- high (Postgrad & above
n2=107)
- - 0.246 0.123 0.125
Not
supported
Price Value to BHV relationship (PRVBHV) [Hypothesis H3b]
Age (AGE)
Grp1- low (<= 25yrs, n1 = 169)
Grp 2- high (> 25yrs, n2 = 88)
- - 0.221 0.194 0.028
Not
supported
Gender (GND)
Grp1- Male (n1=151)
Grp2-Female (n2=106)
- - 0.130 0.029 0.100
Not
supported
Habit to BHV relationship (HABBHV) [Hypothesis H4b]
Age (AGE)
Grp1- low (<= 25yrs, n1 = 169)
Grp 2- high (> 25yrs, n2 = 88)
- - 0.221 0.194 0.028
Not
supported.
Gender (GND)
Grp1- Male (n1=151)
Grp2-Female (n2=106)
- - 0.215 0.126 0.088
Not
Supported
Experience (EXP)
Grp1-low (<= 1 year, n1= 88)
Grp 2- high (> 1 year, n2= 169)
- - -0.005 0.214 0.220
Not
supported
*significant at 95%
10. CONCLUSIONS AND LIMITATIONS
As an emerging service, mobile payment adoption among Indian users is low. Although mobile
phone usage has grown substantially in India, mobile payments which could significantly impact
mobile commerce has not shown proportional growth. This provided us the basic motivation to
investigate academic research in this area. Our search on academic literature showed that mobile
payment has not received due attention in Technology Acceptance studies in India. Mobile
payments studies have shown variation in acceptance and adoption in different countries. This
study addresses this key research gap by extending a recent theory in technology to understand
the mobile payment acceptance amongst the Indian consumers. This study makes contributions
to the body of knowledge of information systems by extending and pilot testing UTAUT2 model
for the specific context of mobile payment systems acceptance in India. The main objective of
this study was to study consumer acceptance of Mobile Payment Systems using UTAUT2 model
and to develop and test suitable moderator(s) in the specific context of Mobile Payments. To
achieve this objective, existing UTAUT2 was used as the research model which consists of
seven variables (performance expectancy, effort expectancy, social influence, facilitating

12. 12
conditions, hedonic motivation, price value and habit) and one dependent variable (intention to
use mobile payment). The moderating effects of age, gender, experience, and level of education
were also studied. Our results showed that user acceptance of mobile payment do not follow
UTAUT2 exactly as in Venkatesh et al. (2012). Direct relationships were found to be very
similar, except for one loop whereas moderating effect relationships were substantially different
from UTAUT2.
This study has some limitations that should be taken into account when interpreting the findings.
First, we did not incorporate actual usage behavior into the proposed model. Second, there may
exist other individual differences and system characteristics variables that can also affect the
intention to use M-payment. Finally the purposive sampling technique poses a limitation as
irrespective of the purposive sampling method used, the sample is prone to researcher bias.
Further, the sample size was relatively low, and attempt was made to overcome this limitation by
using sub sampling by bootstrapping method.
11. REFERENCES
Ashcraft M.H.,(2002). Cognition, third ed., Prentice Hall, Upper Saddle River, NJ.
Au, Y. A. and R.J Kauffman (2008). The economics of mobile payments: understanding
stakeholder issue for an emerging financial technology application, Journal of Electronic
Commerce Research and Applications, 7(2), 141-164.
Chen, J. J., and C. Adams (2005). User acceptance of mobile payments: A theoretical model
for mobile payments. Paper presented at the 5th International Conference on Electronic
Business, Hong Kong, December 5–9.
Cheong, J. H., M. C. Park and J.H. Hwang (2004). Mobile payment adoption in Korea:
Switching from credit card. Paper presented at the ITS 15th Biennial Conference, Berlin,
Germany, September, 4–7.
CNNIC (2010) 26th
Statistical Survey report on the internet development in China. Retrieved
from http://www.cnnic.net.cn/. Accessed on 26 May 2014.

13. 13
Dahlberg, T., N. Mallat and Öörni, A. (2003a). Consumer acceptance of mobile payment
solutions – Ease of use, usefulness and trust. Paper presented at the 2nd
International Conference
on Mobile Business, Vienna, Austria, June 23–24.
Dahlberg, T., N. Mallat, and A. Öörni, (2003b). Trust enhanced technology acceptance model-
consumer acceptance of mobile payment solutions. Paper presented at the 2nd Mobility
Roundtable, Stockholm, Sweden, May 22–23.
Dahlberg, T., N.Mallat, J. Ondrus, and A. Zmijewska (2006). Mobile payment market and
research-past, present and future. Helsinki Mobility Round Table, Helsinki.
Dahlberg, T., N. Mallat, E. Penttinen, and P. Sohlberg (2002). What characteristics of mobile
payment solutions make them valuable to consumers. In Proceedings of the Global Information
Technology Management Conference, USA, New York.
Dewan, S. G., and L.D. Chen (2005). Mobile payment adoption in the USA: A crossindustry,
cross-platform solution. Journal of Information Privacy and Security,1(2), 4–28.
Fornell, C. and D.F. Lacker (1981). Evaluating structural equation models with unobserved
variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
Lucas, H.C Jr.(1978). Empirical evidence for a descriptive model of implementation, MIS
Quarterly, 2(2), 27–41.
Hair, J.F., C.M. Ringle, M. Sarstedt (2011). PLS-SEM: indeed a silver bullet, Journal of
Marketing Theory and Practice, 19 (2), 139–151.
Igbaria, M., S. Parsuraman, (1989). A path analytic study of individual characteristics,
computer anxiety, and attitudes towards microcomputers, Journal of Management, 15(3), 1989,
373–388.
Karnouskos, S. (2004). Mobile payment: a journey through existing procedures and
standardization initiatives. Communications Surveys and Tutorials, IEEE, 6(4), 44-66.
Malhotra, N.K., S.S Kim,and A. Patil (2006). Common method variance in IS research: A
comparison of alternative approaches and a reanalysis of past research. Management Sciences,
52 (12), 1865-1883.

14. 14
Mallat, N. (2004). Theoretical constructs of mobile payment adoption. Paper presented at the
27th Information Systems Research Seminar in Scandinavia (IRIS), Falkenberg, Sweden,
August, 14–17.
Mallat, N., and T. Dahlberg (2005). Consumer and merchant adoption of mobile payment
solutions. Managing business in a multi-channel world: Success factors for e-Business. Hershey,
PA 17033, USA: Idea Group Publishing.
Mallat, N. (2007). Exploring consumer adoption of mobile payments — a qualitative study. The
Journal of Strategic Information Systems, 16 (4) , 413–432.
Nunnally, J.C. (1978). Psychometric theory (2nd
ed.). New York: McGraw-Hill.
Ondrus, J., and Y. Pigneur (2005). A disruption analysis in the mobile payment market. In
System Sciences, 2005. HICSS'05. Proceedings of the 38th Annual Hawaii International
Conference on (pp. 84c-84c). IEEE.
Ondrus, J., and Y. Pigneur (2006). Towards a holistic analysis of mobile payments: A multiple
perspectives approach. Electronic Commerce Research and Applications, 5, 246–257.
Padsakoff, P.M., S.B Mackanzie, J.Y Lee and N.P Padsakoff (2003). Common method
biases in behavioral research: a critical review of the literature and recommended remedies.
Journal of Applied Psychology. 88 (5), 879.
Pousttchi, K. (2008). A modeling approach and reference models for the analysis of mobile
payment use cases. Electronic Commerce Research and Applications, 7(2), 182-201.
.
Scevak, N. (2010. Forrester Research mobile media and messaging forecast, 2010 to 2015
(Western Europe). Retrieved from http://forrester.com/rb/Research/research mobile media and
messaging forecast,2011/q/id/57671/t/2.
Tenenhaus, M., V. E., Vinzi, Y.M. Chatelin, and C. Lauro (2005). PLS path modeling.
Computational statistics and data analysis, 48(1), 159-205.
Torsten, J., A. Gerpott, and K. Kornmeier (2009). Determinants of customer acceptance of
mobile payment systems. International Journal of Electronic Finance, 3(1), 1–30.
Valcourt, E., J.M. Robert and F. Beaulieu (2005). Investigating mobile payment: Supporting
technologies, methods, and use. Paper presented at the International Conference on Wireless and
Mobile Computing, Networking and Communications, Montreal, Canada, August, 22–24.

15. 15
Venkatesh, V., J. Thong and X.Xu (2012). Consumer acceptance and use of information
technology: extending the unified theory of acceptance and use of technology. MIS Quaterly,
36(1),157-178.
Zmijewska, A. and E. Lawrence (2005). Reshaping the framework for analysing success of
mobile payment solutions. In IADIS International Conference on E-Commerce, Porto, Portugal.
Zmijewska, A., E. Lawrence and R. Steele (2004b). Towards understanding of factors
influencing user acceptance of mobile payment systems. Paper presented at the IADIS
International Conference, Madrid, Spain, October 6–9.
Zmud, R.W. (1979). Individual differences and MIS success: a review of the empirical
literature, Management Science, 25(10), 966–979.

16. 16
12. PROPOSED CONTENT OF THE THESIS
Chapter 1 Introduction
1.1 Introduction
1.2 Mobile Payments
1.2.1 Growth of M- payment Adoption
1.2.2 Mobile payments in India
1.3 Motivation and research question
1.3.1 Research Objectives
1.4 Dissertation Outline
Chapter 2 Literature Review
2.1 Introduction
2.2 User acceptance of technology
2.3 Mobile payments and technology acceptance
2.3.1 User Acceptance Theories and Mobile Payments
2.3.2 Country Level Studies in Mobile Payment Adoption
2.3.3 Other related Studies
2.4 Key findings and Research Gap
2.5 Summary
Chapter 3 Research Model and Hypothesis Development
3.1 Introduction
3.2 UTAUT2 and Mobile Payment Acceptance
3.3 Hypothesis Development
3.4 Summary

17. 17
Chapter 4 Research Methodology
4.1 Introduction
4.2 Development of the survey instrument
4.2.1 Target audience and sampling procedure
4.3. Data collection
4.4 Summary
Chapter 5 Data Analyses
5.1 Introduction
5.2 Data preparation and descriptive statistics
5.3 Data analyses
5.4 Measurement Model
5.4.1 Reflective and Construct validity
5.5 Common Method Bias
5.6 Summary
Chapter 6 Result and Discussion
6.1 Introduction
6.2 Structural model
6.2.1 Modeling procedure
6.2.2 Results
6.3 Discussion
6.4 Summary
Chapter 7 Conclusion
7.1 Contributions
7.2 Managerial Implications
7.3 Limitations of the study

18. 18
7.4 Future work
13. Presentation in conference: Nil